Model-Based Diagnosis Preferences and Strategies Representation
with Meta Logic Programming
C.V. Damásio and W. Nejdl and L.M. Pereira and M. Schroeder
Abstract
Preferences and strategies are fundamental to model-based diagnosis,
for specifying preferred and fall-back approaches to the diagnosis
task, both to capture general and domain specific criteria, but also
to tackle the complexity issue by employing heuristics. A formal
framework based on extended logic programming and meta-programs is
provided to represent preferences and strategies required by
model-based diagnosis. This framework is clearer and more expressive
than other approaches that have addressed these problems. We show how
the concepts of preferences and strategies are directly programmed and
captured by logic meta-programming and meta-reasoning methods, and
their implementation techniques.
The paper is intended as proof-of-principle that all concepts needed
by a model-based diagnosis system can represented declaratively and
captured by a logic meta-program. Specialized more efficient
algorithms can be substituted for the simpler proof-of-principle ones
we include, and are the subject of ongoing work.